A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Deep learning-based automatic pipeline system for predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma using computed tomography:A multi-center study

Objective:The assessment of lateral lymph node metastasis(LLNM)in patients with papillary thyroid carcinoma(PTC)holds great significance.This study aims to develop and evaluate a deep learning-based automatic pipeline system(DLAPS)for diagnosing LLNM in PTC using computed tomography(CT).Methods:A total of 1,266 lateral lymph nodes(LLNs)from 519 PTC patients who underwent CT examinations from January 2019 to November 2022 were included and divided into training and validation set,internal test set,pooled external test set,and prospective test set.The DLAPS consists of an auto-segmentation network based on RefineNet model and a classification network based on ensemble model(ResNet,Xception,and DenseNet).The performance of the DLAPS was compared with that of manually segmented DL models,the clinical model,and Node Reporting and Data System(Node-RADS).The improvement of radiologists’diagnostic performance under the DLAPS-assisted strategy was explored.In addition,bulk RNA-sequencing was conducted based on 12 LLNs to reveal the underlying biological basis of the DLAPS.Results:The DLAPS yielded good performance with area under the receiver operating characteristic curve(AUC)of 0.872,0.910,and 0.822 in the internal,pooled external,and prospective test sets,respectively.The DLAPS significantly outperformed clinical models(AUC 0.731,P<0.001)and Node-RADS(AUC 0.602,P<0.001)in the internal test set.Moreover,the performance of the DLAPS was comparable to that of the manually segmented deep learning(DL)model with AUCs ranging 0.814−0.901 in three test sets.Furthermore,the DLAPSassisted strategy improved the performance of radiologists and enhanced inter-observer consistency.In clinical situations,the rate of unnecessary LLN dissection decreased from 33.33%to 7.32%.Furthermore,the DLAPS was associated with the cell-cell conjunction in the microenvironment.Conclusions:Using CT images from PTC patients,the DLAPS could effectively segment and classify LLNs non-invasively,and this system had a good generalization ability and clinical applicability.

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer

Accurate preoperative prediction of lymph node metastasis(LNM)in esophageal cancer(EC)patients is of crucial clinical significance for treatment planning and prognosis.AIM To develop a clinical radiomics nomogram that can predict the preoperative lymph node(LN)status in EC patients.METHODS A total of 32 EC patients confirmed by clinical pathology(who underwent surgical treatment)were included.Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy,and its correlation with LNM was analyzed.Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python.Feature extraction,data dimensionality reduction,and feature selection were performed using XGBoost model and leave-one-out cross-validation.Multivariable logistic regression analysis was used to establish the prediction model,which included radiomics features,LN status from computed tomography(CT)reports,and B7-H3 mRNA expression,represented by a radiomics nomogram.Receiver operating characteristic area under the curve(AUC)and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical application value of the model.RESULTS The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis,and the difference was statistically significant(P<0.05).The AUC value in the receiver operating characteristic(ROC)curve was 0.718(95%CI:0.528-0.907),with a sensitivity of 0.733 and specificity of 0.706,indicating good diagnostic performance.The individualized clinical prediction nomogram included radiomics features,LN status from CT reports,and B7-H3 mRNA expression.The ROC curve demonstrated good diagnostic value,with an AUC value of 0.765(95%CI:0.598-0.931),sensitivity of 0.800,and specificity of 0.706.DCA indicated the practical value of the radiomics nomogram in clinical practice.CONCLUSION This study developed a radiomics nomogram that includes radiomics features,LN status from CT reports,and B7-H3 mRNA expression,enabling convenient preoperative individualized prediction of LNM in EC patients.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma:a multicenter study

Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Immunohistochemical expression of matrix metalloproteinase-9 and 13 in oral squamous cell carcinoma and their role in predicting lymph node metastasis

BACKGROUND One of the main characteristics of oral squamous cell carcinoma(OSCC)is that it metastasizes to cervical lymph nodes frequently with a high degree of local invasiveness.A primary feature of malignant tumors is their penetration of neighboring tissues,such as lymphatic and blood arteries,due to the tumor cells'capacity to break down the extracellular matrix(ECM).Matrix metalloproteinases(MMPs)constitute a family of proteolytic enzymes that facilitate tissue remo-deling and the degradation of the ECM.MMP-9 and MMP-13 belong to the group of extracellular matrix degrading enzymes and their expression has been studied in OSCC because of their specific functions.MMP-13,a collagenase family member,is thought to play an essential role in the MMP activation cascade by breaking down the fibrillar collagens,whereas MMP-9 is thought to accelerate the growth of tumors.Elevated MMP-13 expression has been associated with tumor behavior and patient prognosis in a number of malignant cases.The authors wish to thank Jadhav KB for his valuable opinion during the preparation of the manuscript.

Multiple Node Upset in SEU Hardened Storage Cells

We study the problem of multiple node upset （MNU） using three-dimensional device simulation. The results show the transient floating node and charge lateral diffusion are the key reasons for MNU. We compare the MNU with multiple bit upset （MBU）,and find that their characteristics are different. Methods to avoid MNU are also discussed.

树上的限制性node multicut问题

割集问题在图论和组合优化中占有重要地位,限制性node multicut问题是割集问题的一类比较重要的推广问题。树上的限制性node multicut问题是值得研究的一个问题。首先说明此问题是NP难的,其次用线性规划理论中的互补松弛条件设计了一个近似值2且时间复杂度为O(max{kn,n log n})的算法。并进一步说明了通过算法得到的解具有半整数的性质。

基于改进Node2vec算法的锅炉温度场分割方法研究

针对温度场特征参数差异引发的锅炉温度场分割准确性的问题,以维持温度场特征为目标,引入图结构表达场数据,通过改进Node2vec算法进行聚类分析,进而实现锅炉温度场的最佳分割。该方法基于多维度的特征信息对锅炉温度场实现分割,能够更准确地保留流场特征。在标准数据集上进行了实验验证,结果表明在具有多维度特征的数据集上,所提方法相比其他对比算法在分割效果方面有提升显著。最后将提出的方法用于分割电站锅炉温度场,结果表明该方法可以很好地捕捉温度场数据中的局部和全局特征,且结果具有较好的精确性。

Application value of multi-slice spiral computed tomography for imaging determination of metastatic lymph nodes of gastric cancer

AIM:To evaluate the application value of multi-slice spiral computed tomography(MSCT)for imaging determination of metastatic lymph nodes of gastric cancer and to explore reasonable diagnostic criteria.METHODS:Sixty patients with gastric cancer underwent 64 MSCT scans before operation.Gastric cancer samples and perigastric lymph nodes were obtained after operation,formalin fixation and haematoxylineosin staining.The metastatic conditions of gastric cancer and perigastric lymph nodes were determined under a light microscope.A total of 605 lymph nodes were grouped and assessed according to distribution,size,shape and degree of lymph node enhancement.Then,the findings were compared with the postoperative pathological results.RESULTS:Among 605 lymph nodes,358 were confirmed as metastatic,accounting for 59.2%.A total of535 lymph nodes were detected in original axis images combined with multiplanar reconstruction images of MSCT.The metastatic lymph nodes had specific signs in computed tomography.This study showed that the long diameter of lymph nodes≥8 mm indicated metastasis;the sensitivity and specificity were 79.6%and78.8%,respectively.The difference of the mean value of lymph node enhancement density≥80 Hu indicated metastasis;the sensitivity and specificity were81.6%and 75.6%,respectively.The ratio of short diameter to long diameter of lymph nodes≥0.7 indicated metastasis;the sensitivity and specificity were85.6%and 71.8%,respectively.CONCLUSION:MSCT is a non-invasive and reliable method for preoperative examination of gastric cancer.Sensitivity and specificity for prediction of lymph node metastasis are high.

Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy

Objective:To predict pathological nodal stage of locally advanced rectal cancer by a radiomic method that uses collective features of multiple lymph nodes(LNs)in magnetic resonance images before and after neoadjuvant chemoradiotherapy(NCRT).Methods:A total of 215 patients were included in this study and chronologically divided into the discovery cohort(n=143)and validation cohort(n=72).In total,2,931 pre-NCRT LNs and 1,520 post-NCRT LNs were delineated from all visible rectal LNs in magnetic resonance images.Geometric,first-order and texture features were extracted from each LN before and after NCRT.Collective features are defined as the maximum,minimum,mean,median value and standard deviation of each feature from all delineated LNs of each participant.LN-model is constructed from collective LN features by logistic regression model with L1 regularization to predict pathological nodal stage(ypN0 or ypN+).Tumor-model is constructed from tumor features for comparison by using DeLong test.Results:The LN-model selects 7 features from 412 LN features,and the tumor-model selects 7 features from 82 tumor features.The area under the receiver operating characteristic curve(AUC)of LN-model in the discovery cohort is 0.818[95%confidence interval(95%CI):0.745-0.878],significantly(Z=2.09,P=0.037)larger than 0.685(95%CI:0.602-0.760)of the tumor-model.The AUC of LN-model in validation cohort is 0.812(95%CI:0.703-0.895),significantly(Z=3.106,P=0.002)larger than 0.517(95%CI:0.396-0.636)of the tumor-model.Conclusions:The usage of collective features from all visible rectal LNs performs better than the usage of tumor features for the prediction of pathological nodal stage of locally advanced rectal cancer.

Prognostic value of lymph node metastasis in patients with T1-stage colorectal cancer from multiple centers in China

AIM To explore the features and prognostic value of lymph node metastasis in patients with T1-stage colorectal cancer(CRC).METHODS In all,321 cases of T1-stage CRC were selected from 10132 patients with CRC who received surgical therapy in six large-scale hospitals in China and were retrospectively analyzed. Univariate and multivariate analyses were performed to analyze the risk factors for lymphatic metastasis. A survival analysis was then performed to analyze the prognostic value of lymph node metastasis.RESULTS The occurrence rate of T1 stage was 3.17%(321/10132);of these patients,the lymph node metastasis rate was 8.41%(27/321),and the non-lymph node metastasis rate was 91.59%(294/321). Univariate analysis showed that preoperative serum CEA,preoperative serum CA199,preoperative serum CA724,vascular invasion,and degree of differentiation were associated with lymph node metastasis in T1-stage CRC(P < 0.05 for all). Multivariate analysis indicated that preoperative serum CA724,vascular invasion,and degree of differentiation were closely related to lymph node metastasis(P < 0.05 for all). Log-rank survival analysis showed that age,preoperative serum CEA,preoperative serum CA199,vascular invasion,degree of differentiation,and lymph node metastasis(χ2 = 24.180,P < 0.001) were predictors of 5-year overall survival(OS)(P < 0.05 for all). COX regression analysis demonstrated that preoperative serum CA199 and lymph node metastasis(HR = 5.117;P < 0.05;95%CI: 0.058-0.815) were independent prognostic indicators of 5-year OS in patients with T1-stage CRC(P < 0.05 for both). CONCLUSION The morbidity of T1-stage CRC was 3.17% for all CRC cases. Preoperative serum CA724,vascular invasion,and degree of differentiation are independent risk factors for lymph node metastasis. Lymph node metastasis is an independent prognostic factor for OS in patients with T1-stage CRC.

A method to simulate multilayer welding process： Node dynamic relaxation method

A new method called node dynamic relaxation is proposed to simulate multilayer welding. A two dimensional plane strain model for multilayer welding is simulated and the results show that mesh distortion can be decreased, and it is also found that the node dynamic relaxation is a kind of method to calculate welding deformation accurately by comparing experiment results with simulation results.

Endoscopic ultrasound elastography for evaluation of lymph nodes and pancreatic masses:A multicenter study

AIM:To evaluate the ability of endoscopic ultrasound(EUS) elastography to distinguish benign from malignant pancreatic masses and lymph nodes.METHODS:A multicenter study was conducted and included 222 patients who underwent EUS examination with assessment of a pancreatic mass(n=121) or lymph node(n=101).The classification as benign or malignant,based on the real time elastography pattern,was compared with the classif ication based on the B-mode EUS images and with the fi nal diagnosis obtained by EUS-guided fi ne needle aspiration(EUS-FNA) and/or by surgical pathology.An interobserver study was performed.RESULTS:The sensitivity and specificity of EUS elastography to differentiate benign from malignant pancreatic lesions are 92.3% and 80.0%,respectively,compared to 92.3% and 68.9%,respectively,for the conventional B-mode images.The sensitivity and specificity of EUS elastography to differentiate benign from malignant lymph nodes was 91.8% and 82.5%,respectively,compared to 78.6% and 50.0%,respectively,for the B-mode images.The kappa coefficient was 0.785 for the pancreatic masses and 0.657 for the lymph nodes.CONCLUSION:EUS elastography is superior compared to conventional B-mode imaging and appears to be able to distinguish benign from malignant pancreatic masses and lymph nodes with a high sensitivity,specificity and accuracy.It might be reserved as a second line examination to help characterise pancreatic masses after negative EUS-FNA and might increase the yield of EUS-FNA for lymph nodes.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

Diagnostic ability of multi-detector spiral computed tomography for pathological lymph node metastasis of advanced gastric cancer

BACKGROUND The reliability of preoperative nodal diagnosis of advanced gastric cancer by multi-detector spiral computed tomography(MDCT)is still unclear.AIM To examine the diagnostic ability of MDCT more precisely by using data on intranodal pathological metastatic patterns.METHODS A total of 108 patients with advanced gastric cancer who underwent MDCT and curative gastrectomy at Kanazawa Medical University Hospital were enrolled in this study.The nodal sizes measured on computed tomography(CT)images were compared with the pathology results.A receiver-operating characteristic curve was constructed,from which the critical value(CV)was calculated by using the data of the first 69 patients retrospectively.By using the CV,sensitivity and specificity were calculated with prospectively collected data from 39 consecutive patients.This enabled a more precise one-to-one correspondence of lymph nodes between CT and pathological examination by using the size data of lymph node mapping.The intranodal pathological metastatic patterns were classified into the following four types:Small nodular,peripheral,large nodular,and diffuse.RESULTS Although all the cases were clinically suspected as having metastasis,81 had lymph node metastasis and 27 had no metastasis.The number of dissected,detected on CT,and metastatic nodes were,4241,897,and 801,respectively.The CV obtained from the receiver-operating characteristic was 7.6 mm for the long axis.The sensitivity was 91.4%and the specificity was 47.3%in the prospective phase.The large nodular and diffuse metastases were easy to diagnose becausemetastatic nodes with a large axis often exhibit these forms.CONCLUSION The ability of MDCT to contribute to a nodal diagnosis of advanced gastric cancer was examined prospectively with precise size data from node mapping,using a CV of 7.6 mm for the long axis that was calculated from the retrospectively collected data.The sensitivity was as high as 91%,and would be improved when referring to the enhanced patterns.However,its specificity was as low as 47%,because most of metastatic nodes in gastric cancer being small in size.The small nodular or peripheral type metastatic nodes were often small and considered difficult to diagnose.

Loading System for Full-Scale Heavy-Duty Support Node Test with Multi-Directional Loading Requirements

This paper presents the design, analysis and experimental study of a loading system for heavy-duty nodes test based on a large-scale multi-directional in-plane loading device, which has been used in a full-scale heavy-duty support node test. Test loads of the support reached 6 567 kN with multi-directional loading requirements, which outrange the capacity of the available loading devices. Through the reinforcement of a large-scale multi-directional inplane loading device, the innovative design of a self-balanced load transferring device, and other arrangement considerations of the loading system, the test was implemented and the loading capacity of the ring was considerably enlarged. Due to the heavy loading requirements, some checking computations of the ring and the load transferring device outranged the limit of the Chinese national code "Code for Design of Steel Structures (GB 50017—2003)", thus elastic-plastic finite element (FE) analysis was carried out on the two devices, and also the real-time monitoring on the whole loading systems during experiments to ensure test safety. FE analysis and test results show that the loading system worked elastically during experiments.

Development and multicenter validation of a nomogram for preoperative prediction of lymph node positivity in pancreatic cancer(NeoPangram)

Background:Neoadjuvant therapy is associated with nodal downstaging and improved oncological outcomes in patients with lymph node(LN)-positive pancreatic cancer.This study aimed to develop and validate a nomogram to preoperatively predict LN-positive disease.Methods:A total of 558 patients with resected pancreatic cancer were randomly and equally divided into development and internal validation cohorts.Multivariate logistic regression analysis was used to construct the nomogram.Model performance was evaluated by discrimination,calibration,and clinical usefulness.An independent multicenter cohort consisting of 250 patients was used for external validation.Results:A four-marker signature was built consisting of carbohydrate antigen 19–9(CA19–9),CA125,CA50,and CA242.A nomogram was constructed to predict LN metastasis using three predictors identified by multivariate analysis:risk score of the four-marker signature,computed tomography-reported LN status,and clinical tumor stage.The prediction model exhibited good discrimination ability,with C-indexes of 0.806,0.742 and 0.763 for the development,internal validation,and external validation cohorts,respectively.The model also showed good calibration and clinical usefulness.A cut-off value(0.72)for the probability of LN metastasis was determined to separate low-risk and high-risk patients.Kaplan-Meier survival analysis revealed a good agreement of the survival curves between the nomogram-predicted status and the true LN status.Conclusions:This nomogram enables the identification of pancreatic cancer patients at high risk for LN positivity who may have more advanced disease and thus could potentially benefit from neoadjuvant therapy.

On-demand multicast routing protocol based on node classification in MANET

An improved on-demand multicast routing protocol(ODMRP), node classification on-demand multicast routing protocol(NC-ODMRP), which is based on node classification in mobile ad hoc networks was proposed. NC-ODMRP classifies nodes into such three categories as ordinary node, forwarding group(FG) node, neighbor node of FG node according to their history forwarding information. The categories are distinguished with different weights by a weight table in the nodes. NC-ODMRP chooses the node with the highest weight as an FG node during the setup of forwarding group, which reduces a lot of redundant FG nodes by sharing more FG nodes between different sender and receiver pairs. The simulation results show that NC-ODMRP can reduce more than 20% FG number of ODMRP, thus enhances nearly 14% data forwarding efficiency and 12% energy consumption efficiency when the number of multicast senders is more than 5.